Tom Bogan – The Importance of Being Agile

Tom Bogan is CEO of Adaptive Insights, a Workday company whose business planning cloud automates previously manual planning tasks—for finance, sales, and workforce planning—and supplements those capabilities with enterprise-scale reporting and analytics. Tom joined Adaptive Insights when it was at $40 million in revenue. Under his leadership, in 2017 Adaptive Insights topped $100 million in annual recurring revenue (ARR), a watershed milestone that only 1% of cloud software companies ever reach. Then, in June of 2018, three days before the company was to go public, Bogan and his team struck a deal with Workday, a leader in cloud corporate performance management, to acquire Adaptive Insights for $1.55 billion.

Now a Workday company, Adaptive Insights continues to cook up ways to help businesses see planning as a competitive advantage. Most recently, Adaptive Insights commissioned Plan to Win: Achieving business agility in the age of urgency. The new book seemed like a good reason to sit down with Tom and get his thoughts on the origins of Plan to Win, the state of cloud software tools, and how businesses will plan years from now.

Let’s start with the book. What prompted it?

We’re always talking to customers. In doing so we learned that many felt there simply wasn’t much written about why modern planning was critical to the success of a business. They’d gone through the transformation themselves—from slow, manual, siloed static planning to automated, collaborative, and continuous active planning—so they knew what it had done for their business agility. They wanted something they could share with others, a book they could pass along to colleagues in their organization who may still be stuck doing static planning, or might be considering a change but not sure how to map their journey.

The book isn’t just a reflection of our world view on planning. That’s in there, but it’s also a researched exploration of how businesses have used planning over time, and how the digital age made those old planning processes obsolete. And it’s filled with lessons we’ve learned from our customers, including a few whose experiences are profiled in the book. It’s a helpful tool for both companies new to a cloud planning process and for our customers who continue to expand their vision for business planning in their organizations.

We’ve known for a while that static planning doesn’t work. But for many companies, budgeting remains a painful annual ritual. Why haven’t they been able to break out of it?

A big reason is the tools they’re using. Most organizations still use spreadsheets. And unless you’re a very small company, spreadsheets quickly hinder your ability to plan across a wide population in your company.

Business and market conditions don’t change just once a year—they change constantly. So if you plan annually, you’re creating a plan that’s obsolete almost as soon as it’s done. So to operate with agility, you need to be able to update plans, budgets, and forecasts continuously, not just once a year. Many of our customers update their plans quarterly and some do it monthly, or at least as often as conditions change.

And as businesses grow, they realize that spreadsheet planning is cumbersome because they make it hard to coordinate all resources across the organization. Cloud-based planning enables a more agile process because everybody is using the same cloud system—not dozens of individual spreadsheets traded by email. They all work from the same data. There’s a single source of truth.

Putting planning into the cloud, having a flexible modeling platform, and deploying capabilities that allow you to engage in both financial and operational planning—this is what is helping break people out of static planning.

It seems like we've seen a mass proliferation of data analytics tools, data visualization tools, and so on. Companies seem to have one of each. Does that help planning or does it actually make it more fragmented?

I think it helps. When companies have a good understanding of their operations and the operating data, it inspires them to do richer planning.

For example, when I started doing planning in companies, we'd look at a revenue forecast. It would be more trend-based: What percentage are we going to increase this year? Today when we do our sales planning, we're looking at the number of reps we have and the number of leads we're generating. What are those conversion rates from marketing leads to sales opportunities? How many sales opportunities can our reps execute against? What are the expected win rates? All that rich operating data is now part of the plan.

Now apply that to agility. If our actuals are either better or worse than the assumptions in the plan, we can change and update that plan. Those regular updates allow you to course-correct when needed. Visualization systems, the tendency to collect metrics and KPIs and the ability to see those through the various analytics tools—all this provides a strong foundation because the operating metrics become the drivers or the assumptions in the plan. We built the plan and the models on top of that analytical data. They fit together beautifully. So the proliferation of analytics tools, together with the planning model, is very synergistic.

Workday's focus is primarily finance and HR. Does that constrain what you get from customers and does that kind of make it competitively disadvantaged for you because you don't have access to too much operational data?

That’s a great question. In the Adaptive Insights Business Planning Cloud, we certainly integrate with Workday solutions, but we also integrate with all other systems as well: General ledger systems from Oracle, SAP, NetSuite, and others; CRM systems like Salesforce; marketing automation platforms like Marketo, HubSpot or Eloqua—we integrate with all of these systems so that data is feeding our assumptions.

We describe the Business Planning Cloud as “best in class, better in suite.” This means if you’re using Adaptive Insights on its own or with a non-Workday ERP system, you’re still getting the best, most highly-rated planning solution in the industry. But it gets even better if you choose to implement solutions from Workday. We have not only HR and financial data from Workday business systems, but we can add information from other GL systems, from sub-ledgers, and from operating analytical data. We can either bring that natively into Adaptive Insights through our interfaces or we can bring it in through Workday’s Prism Analytics.

SAP has something called Integrated Business Planning, and they go into all kinds of manufacturing and transportation and distribution planning. Does that put you at a disadvantage?

Not at all, because we place no limitations on the amount of information or the number of dimensions you can build into a model. And it’s important to remember that Adaptive Insights is a broad-based modeling platform. You can model any business scenario in Adaptive Insights, just as you could in a spreadsheet.

For operational planning, we pre-build some functionality. For example, in sales planning, we know that most organizations will want to plan around the number of reps they have, by market segments, and possibly by vertical industry. They'll want to do territory planning and to measure key aspects or dimensions. We provide the framework that allows them to do that. Our customers use that framework to build models and solutions that are specific to their business needs.

One customer, an airline, uses Adaptive Insights to run route profitability analysis for their routes all over the world so they know which are the most profitable. Another is P.F. Chang’s, who is featured in the book. They model the operations at a restaurant level. Again, customers and users model any aspect of their business on the platform.

You mentioned how cloud has evolved planning; made it much more collaborative, much more available to a broader audience, etc. What is machine learning going to do to planning?

Great question. It's going to change everything. We think that planning is one of the areas that will be more impacted by ML than almost any other space. In the short term, we'll be able to leverage machine learning to do things like anomaly detection and to determine when information in a plan looks aberrant relative to what we'd expect based on analysis of history and trends. We'll be able to flag exceptions. The user will be able to decide whether they're appropriate exceptions or whether it's something that has to be looked at further.

Then we'll learn from that. We'll be able to better anticipate and predict what the data should look like. That's the short term.

Going forward, I think we'll be able to leverage machine learning to create additional planned scenarios automatically. We’ll identify the most critical assumptions or drivers in the plan. We’ll take the heavy lifting out of scenario planning.

We want to shift where people are spending their time, from creating the plan and crunching numbers to running analyses and really understanding what’s in the plan. What are the critical assumptions? What kind of scenarios can I run? Then I believe we'll also be able to assess confidence. A CFO can go to the board that this is a 75% probability plan or maybe it's a 25% probability plan. They can know where they stand before going in.

Improvement will be gradual, and then it will accelerate once it hits critical mass. I’m sure years from now, we’ll do planning in a completely different way. But I’m hopeful that five years from now, we’ll do planning in a completely different way.

Five to 10 years sounds realistic. But many people make it sound like it will happen tomorrow.

It depends on what we're talking about. Anomaly detection will happen sooner but that’s only the tip of the iceberg for machine learning. I think about the world in which we can automatically create plans and do a first pass. That’s a little further out.

Do you think planners are preparing for that? I talk to some companies where they have literally hundreds of planners, category planners, assortment planners in the CPG industry and so on. It just seems like there is a lot of labor in planning. Are you helping planners look forward to the day where machines will be their assistants?

Yes. Whenever you talk about automation, everybody assumes we’re going to eliminate jobs. That’s not the goal. It’s to reduce the number of labor hours on preparing a plan so you have more time for strategic analysis. That’s part of the return companies and organizations will experience.

I think the point you're making applies just about everywhere. Automation takes over the more mundane, routine tasks, and humans move up to a higher value in the workstream. What else did you learn from preparing this book?

Two things. One is that there was strong affirmation of the need for active planning—planning that’s continuous, with monthly or at least quarterly updates to the plan, and with the best practice of rolling forecasts, where you’re not bound by this artificial annual time boundary. The book confirms that this is an important trend for companies.

The other is the importance of machine learning. The leading thinkers, and some are featured in the book, really believe there's a huge opportunity to leverage machine learning in the planning process, to make it more agile. We agree and believe that the tomorrow’s winners will be the most agile.

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Tom Bogan – The Importance of Being Agile

Tom Bogan is CEO of Adaptive Insights, a Workday company whose business planning cloud automates previously manual planning tasks—for finance, sales, and workforce planning—and supplements those capabilities with enterprise-scale reporting and analytics. Tom joined Adaptive Insights when it was at $40 million in revenue. Under his leadership, in 2017 Adaptive Insights topped $100 million in annual recurring revenue (ARR), a watershed milestone that only 1% of cloud software companies ever reach. Then, in June of 2018, three days before the company was to go public, Bogan and his team struck a deal with Workday, a leader in cloud corporate performance management, to acquire Adaptive Insights for $1.55 billion.

Now a Workday company, Adaptive Insights continues to cook up ways to help businesses see planning as a competitive advantage. Most recently, Adaptive Insights commissioned Plan to Win: Achieving business agility in the age of urgency. The new book seemed like a good reason to sit down with Tom and get his thoughts on the origins of Plan to Win, the state of cloud software tools, and how businesses will plan years from now.

Let’s start with the book. What prompted it?

We’re always talking to customers. In doing so we learned that many felt there simply wasn’t much written about why modern planning was critical to the success of a business. They’d gone through the transformation themselves—from slow, manual, siloed static planning to automated, collaborative, and continuous active planning—so they knew what it had done for their business agility. They wanted something they could share with others, a book they could pass along to colleagues in their organization who may still be stuck doing static planning, or might be considering a change but not sure how to map their journey.

The book isn’t just a reflection of our world view on planning. That’s in there, but it’s also a researched exploration of how businesses have used planning over time, and how the digital age made those old planning processes obsolete. And it’s filled with lessons we’ve learned from our customers, including a few whose experiences are profiled in the book. It’s a helpful tool for both companies new to a cloud planning process and for our customers who continue to expand their vision for business planning in their organizations.

We’ve known for a while that static planning doesn’t work. But for many companies, budgeting remains a painful annual ritual. Why haven’t they been able to break out of it?

A big reason is the tools they’re using. Most organizations still use spreadsheets. And unless you’re a very small company, spreadsheets quickly hinder your ability to plan across a wide population in your company.

Business and market conditions don’t change just once a year—they change constantly. So if you plan annually, you’re creating a plan that’s obsolete almost as soon as it’s done. So to operate with agility, you need to be able to update plans, budgets, and forecasts continuously, not just once a year. Many of our customers update their plans quarterly and some do it monthly, or at least as often as conditions change.

And as businesses grow, they realize that spreadsheet planning is cumbersome because they make it hard to coordinate all resources across the organization. Cloud-based planning enables a more agile process because everybody is using the same cloud system—not dozens of individual spreadsheets traded by email. They all work from the same data. There’s a single source of truth.

Putting planning into the cloud, having a flexible modeling platform, and deploying capabilities that allow you to engage in both financial and operational planning—this is what is helping break people out of static planning.

It seems like we've seen a mass proliferation of data analytics tools, data visualization tools, and so on. Companies seem to have one of each. Does that help planning or does it actually make it more fragmented?

I think it helps. When companies have a good understanding of their operations and the operating data, it inspires them to do richer planning.

For example, when I started doing planning in companies, we'd look at a revenue forecast. It would be more trend-based: What percentage are we going to increase this year? Today when we do our sales planning, we're looking at the number of reps we have and the number of leads we're generating. What are those conversion rates from marketing leads to sales opportunities? How many sales opportunities can our reps execute against? What are the expected win rates? All that rich operating data is now part of the plan.

Now apply that to agility. If our actuals are either better or worse than the assumptions in the plan, we can change and update that plan. Those regular updates allow you to course-correct when needed. Visualization systems, the tendency to collect metrics and KPIs and the ability to see those through the various analytics tools—all this provides a strong foundation because the operating metrics become the drivers or the assumptions in the plan. We built the plan and the models on top of that analytical data. They fit together beautifully. So the proliferation of analytics tools, together with the planning model, is very synergistic.

Workday's focus is primarily finance and HR. Does that constrain what you get from customers and does that kind of make it competitively disadvantaged for you because you don't have access to too much operational data?

That’s a great question. In the Adaptive Insights Business Planning Cloud, we certainly integrate with Workday solutions, but we also integrate with all other systems as well: General ledger systems from Oracle, SAP, NetSuite, and others; CRM systems like Salesforce; marketing automation platforms like Marketo, HubSpot or Eloqua—we integrate with all of these systems so that data is feeding our assumptions.

We describe the Business Planning Cloud as “best in class, better in suite.” This means if you’re using Adaptive Insights on its own or with a non-Workday ERP system, you’re still getting the best, most highly-rated planning solution in the industry. But it gets even better if you choose to implement solutions from Workday. We have not only HR and financial data from Workday business systems, but we can add information from other GL systems, from sub-ledgers, and from operating analytical data. We can either bring that natively into Adaptive Insights through our interfaces or we can bring it in through Workday’s Prism Analytics.

SAP has something called Integrated Business Planning, and they go into all kinds of manufacturing and transportation and distribution planning. Does that put you at a disadvantage?

Not at all, because we place no limitations on the amount of information or the number of dimensions you can build into a model. And it’s important to remember that Adaptive Insights is a broad-based modeling platform. You can model any business scenario in Adaptive Insights, just as you could in a spreadsheet.

For operational planning, we pre-build some functionality. For example, in sales planning, we know that most organizations will want to plan around the number of reps they have, by market segments, and possibly by vertical industry. They'll want to do territory planning and to measure key aspects or dimensions. We provide the framework that allows them to do that. Our customers use that framework to build models and solutions that are specific to their business needs.

One customer, an airline, uses Adaptive Insights to run route profitability analysis for their routes all over the world so they know which are the most profitable. Another is P.F. Chang’s, who is featured in the book. They model the operations at a restaurant level. Again, customers and users model any aspect of their business on the platform.

You mentioned how cloud has evolved planning; made it much more collaborative, much more available to a broader audience, etc. What is machine learning going to do to planning?

Great question. It's going to change everything. We think that planning is one of the areas that will be more impacted by ML than almost any other space. In the short term, we'll be able to leverage machine learning to do things like anomaly detection and to determine when information in a plan looks aberrant relative to what we'd expect based on analysis of history and trends. We'll be able to flag exceptions. The user will be able to decide whether they're appropriate exceptions or whether it's something that has to be looked at further.

Then we'll learn from that. We'll be able to better anticipate and predict what the data should look like. That's the short term.

Going forward, I think we'll be able to leverage machine learning to create additional planned scenarios automatically. We’ll identify the most critical assumptions or drivers in the plan. We’ll take the heavy lifting out of scenario planning.

We want to shift where people are spending their time, from creating the plan and crunching numbers to running analyses and really understanding what’s in the plan. What are the critical assumptions? What kind of scenarios can I run? Then I believe we'll also be able to assess confidence. A CFO can go to the board that this is a 75% probability plan or maybe it's a 25% probability plan. They can know where they stand before going in.

Improvement will be gradual, and then it will accelerate once it hits critical mass. I’m sure years from now, we’ll do planning in a completely different way. But I’m hopeful that five years from now, we’ll do planning in a completely different way.

Five to 10 years sounds realistic. But many people make it sound like it will happen tomorrow.

It depends on what we're talking about. Anomaly detection will happen sooner but that’s only the tip of the iceberg for machine learning. I think about the world in which we can automatically create plans and do a first pass. That’s a little further out.

Do you think planners are preparing for that? I talk to some companies where they have literally hundreds of planners, category planners, assortment planners in the CPG industry and so on. It just seems like there is a lot of labor in planning. Are you helping planners look forward to the day where machines will be their assistants?

Yes. Whenever you talk about automation, everybody assumes we’re going to eliminate jobs. That’s not the goal. It’s to reduce the number of labor hours on preparing a plan so you have more time for strategic analysis. That’s part of the return companies and organizations will experience.

I think the point you're making applies just about everywhere. Automation takes over the more mundane, routine tasks, and humans move up to a higher value in the workstream. What else did you learn from preparing this book?

Two things. One is that there was strong affirmation of the need for active planning—planning that’s continuous, with monthly or at least quarterly updates to the plan, and with the best practice of rolling forecasts, where you’re not bound by this artificial annual time boundary. The book confirms that this is an important trend for companies.

The other is the importance of machine learning. The leading thinkers, and some are featured in the book, really believe there's a huge opportunity to leverage machine learning in the planning process, to make it more agile. We agree and believe that the tomorrow’s winners will be the most agile.